High-throughput experimentation (HTE) is a form of accelerated testing which allows for many hundreds or thousands of experiments to be conducted in parallel or in rapid sequence. Recent advances in chemical reaction miniaturization have enabled HTE application toward chemical reaction exploration, and the resultant datasets present exciting opportunities for the incorporation of machine learning (ML) with organic chemistry to expedite reaction optimization and discovery.
Disclosed herein is a modular HTE chemistry reaction platform with rapid and inexpensive data acquisition capabilities for training ML algorithms on organic chemistry. Comprising almost entirely off-the-shelf components and algorithms which will be made open-source, this platform facilitates data democratization through distributed generation. Underpinning this workflow is an innovative titration-based analysis method for semi-automated and quantitative conversion data acquisition at a rate of under fifteen seconds per sample. Requisite to this platform’s success are solutions to solid and liquid reagent distribution, reaction incubation, and fast, quantitative reaction analysis which is demonstrated in a proof-of-concept screening of the SNAr reaction toward the synthesis of proteolysis targeting chimera (PROTACs). It is hoped this platform lowers the barrier for entry to HTE for chemists through its modularity, approachability, and low operating costs. / 2024-06-16T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/44797 |
| Date | 16 June 2022 |
| Creators | Fraser, Douglas Gordon |
| Contributors | Beeler, Aaron B. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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